Graph-Based Maximum Connected-Component Learning Algorithm for Small Target Detection in Maritime Radars

Anomaly detection needs to learn one-class classifiers from normal instances in observation or feature spaces. In the Neyman–Pearson criterion, the design of one-class classifiers boils down to finding the minimal-volume decision region subject to the error probability of normal instances no larger...

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Veröffentlicht in:	IEEE transactions on aerospace and electronic systems Jg. 61; H. 1; S. 250 - 265
Hauptverfasser:	Bai, Xiaohui, Xu, Shuwen, Guo, Zixun, Shui, Penglang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.02.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Anomalies Anomaly detection Classification algorithms convexhull learning algorithm Convexity Detectors False alarms Feature extraction feature-based detection Graph theory graph-based maximum connected-component (GMCC) learning algorithm Level set Machine learning Probability density functions Radar sea-surface small target detection Search algorithms Signal processing algorithms Target detection Training
ISSN:	0018-9251, 1557-9603
Online-Zugang:	Volltext
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